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Added notebook for how to use LRScheduler #79

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Added notebook for how to use LRScheduler #79

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302 changes: 302 additions & 0 deletions how-to-guides/12-lr-scheduler.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- ---\n",
"title: <required-title>\n",
"date: 2022-03-11\n",
"downloads: true\n",
"weight: 12\n",
"sidebar: True\n",
"summary: This example demonstrates how to use the [torch.optim.lr_scheduler](https://pytorch.org/docs/stable/optim.html#torch.optim.lr_scheduler.LambdaLR) to adjust the learning rate of a model.\n",
"tags:\n",
" - lr scheduler\n",
"--- -->\n",
"\n",
"# How to use LR-Schedulers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This how-to guide demonstrates how we can use LR-Schedulers to adjust the learning rate of a model."
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@divyanshugit please add more context and links on ignite docs for the functionalities used in the NB.

]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"! pip install pytorch-ignite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.optim as optim\n",
"from torch.optim.lr_scheduler import ExponentialLR\n",
"\n",
"from ignite.engine import Engine, Events\n",
"from ignite.handlers import create_lr_scheduler_with_warmup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a `Dummy Trainer`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def train_step(e, b):\n",
" print(trainer.state.epoch, trainer.state.iteration, \" | \", optimizer.param_groups[0][\"lr\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"trainer = Engine(train_step)\n",
"optimizer = optim.SGD([torch.tensor([0.1])], lr=0.1234)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initiate a `LRScheduler`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"torch_lr_scheduler = ExponentialLR(optimizer=optimizer, gamma=0.5)\n",
"\n",
"data = [0] * 8\n",
"epoch_length = len(data)\n",
"warmup_duration = 5\n",
"scheduler = create_lr_scheduler_with_warmup(torch_lr_scheduler,\n",
" warmup_start_value=0.0,\n",
" warmup_duration=warmup_duration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trigger LR-Scheduler:\n",
"\n",
" - Step 1: Trigger scheduler on interation_started events before reaching warm-up.\n",
" - Step 2: Trigger scheduler on epoch_started events after the warm-up. \n",
"\n",
"Note: Epochs are 1-based, thus we do 1 + warmup_duration / epoch_length \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ignite.engine.events.RemovableEventHandle at 0x7fb7a979bfa0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined_events = Events.ITERATION_STARTED(event_filter=lambda _, __: trainer.state.iteration <= warmup_duration)\n",
"combined_events |= Events.EPOCH_STARTED(event_filter=lambda _, __: trainer.state.epoch > 1 + warmup_duration / epoch_length)\n",
"trainer.add_event_handler(combined_events, scheduler)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Execute Trainer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 1 | 0.0\n",
"1 2 | 0.03085\n",
"1 3 | 0.0617\n",
"1 4 | 0.09255\n",
"1 5 | 0.1234\n",
"1 6 | 0.1234\n",
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"1 8 | 0.1234\n",
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"2 15 | 0.0617\n",
"2 16 | 0.0617\n",
"3 17 | 0.03085\n",
"3 18 | 0.03085\n",
"3 19 | 0.03085\n",
"3 20 | 0.03085\n",
"3 21 | 0.03085\n",
"3 22 | 0.03085\n",
"3 23 | 0.03085\n",
"3 24 | 0.03085\n",
"4 25 | 0.015425\n",
"4 26 | 0.015425\n",
"4 27 | 0.015425\n",
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"4 31 | 0.015425\n",
"4 32 | 0.015425\n",
"5 33 | 0.0077125\n",
"5 34 | 0.0077125\n",
"5 35 | 0.0077125\n",
"5 36 | 0.0077125\n",
"5 37 | 0.0077125\n",
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"5 40 | 0.0077125\n",
"6 41 | 0.00385625\n",
"6 42 | 0.00385625\n",
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"6 44 | 0.00385625\n",
"6 45 | 0.00385625\n",
"6 46 | 0.00385625\n",
"6 47 | 0.00385625\n",
"6 48 | 0.00385625\n",
"7 49 | 0.001928125\n",
"7 50 | 0.001928125\n",
"7 51 | 0.001928125\n",
"7 52 | 0.001928125\n",
"7 53 | 0.001928125\n",
"7 54 | 0.001928125\n",
"7 55 | 0.001928125\n",
"7 56 | 0.001928125\n",
"8 57 | 0.0009640625\n",
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},
{
"data": {
"text/plain": [
"State:\n",
"\titeration: 80\n",
"\tepoch: 10\n",
"\tepoch_length: 8\n",
"\tmax_epochs: 10\n",
"\toutput: <class 'NoneType'>\n",
"\tbatch: 0\n",
"\tmetrics: <class 'dict'>\n",
"\tdataloader: <class 'list'>\n",
"\tseed: <class 'NoneType'>\n",
"\ttimes: <class 'dict'>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.run(data, max_epochs=10)"
]
}
],
"metadata": {
"accelerator": "GPU",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}